LanguageTool

beginner

Open source proofreading software for grammar and spell checking

Overview

LanguageTool is an open-source multilingual grammar, style, and spell checker that serves as a powerful alternative to proprietary writing assistants. Originally developed as a Java-based application, it has evolved into a comprehensive proofreading platform that supports over 30 languages and provides detailed grammatical analysis, style suggestions, and contextual spell checking. The software uses rule-based checking combined with statistical language models to detect errors that simple spell checkers miss, making it particularly valuable for professional writing, academic work, and content creation across multiple languages. This Docker deployment leverages the erikvl87/languagetool image to provide a self-hosted HTTP API server that processes text through RESTful endpoints. The configuration includes memory optimization settings and support for n-gram datasets, which significantly improve the quality of suggestions by providing statistical context for word usage patterns. The containerized setup eliminates the complexity of Java environment management while providing a scalable backend that can serve multiple clients simultaneously. This stack is ideal for organizations requiring privacy-compliant grammar checking, developers building writing applications, educational institutions needing multilingual support, and content teams who want to integrate advanced proofreading into their workflows. Unlike cloud-based alternatives, this self-hosted solution keeps sensitive documents within your infrastructure while providing enterprise-grade language processing capabilities that can be customized with additional rules and language models.

Key Features

RESTful API server with /v2/check endpoint for programmatic text analysis and grammar checking
Support for 30+ languages including English, German, French, Spanish, Portuguese, and many others
N-gram statistical language model integration for context-aware suggestions and improved accuracy
Configurable Java heap memory allocation (512MB-2GB) for handling large documents and concurrent requests
Rule-based grammar checking with over 4,000 error patterns for comprehensive text analysis
Style and tone suggestions beyond basic grammar, including wordiness and clarity improvements
Compatible backend for browser extensions, LibreOffice, and third-party writing applications
JSON response format with detailed error positioning, suggestions, and confidence ratings

Common Use Cases

1Self-hosted grammar checking service for enterprises with strict data privacy requirements
2Backend API for custom writing applications, content management systems, and editorial workflows
3Educational institutions providing multilingual writing assistance without external data sharing
4Content marketing teams needing consistent proofreading across multiple languages and writers
5Technical documentation teams integrating automated grammar checking into CI/CD pipelines
6Publishing companies requiring advanced style checking and editorial consistency tools
7Government agencies needing compliant language processing without cloud dependencies

Prerequisites

Minimum 2GB RAM available for Java heap allocation and n-gram model loading
Port 8010 available for LanguageTool HTTP API server (configurable via LT_PORT environment variable)
At least 1GB disk space for n-gram language models and application data
Basic understanding of REST API integration for connecting client applications
Network access for downloading n-gram datasets during initial setup
Java application troubleshooting knowledge for memory tuning and performance optimization

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  languagetool: 
3    image: erikvl87/languagetool:latest
4    container_name: languagetool
5    restart: unless-stopped
6    ports: 
7      - "${LT_PORT:-8010}:8010"
8    environment: 
9      - langtool_languageModel=/ngrams
10      - Java_Xms=512m
11      - Java_Xmx=2g
12    volumes: 
13      - ./ngrams:/ngrams

.env Template

.env

1# LanguageTool Configuration
2LT_PORT=8010

Usage Notes

1API at http://localhost:8010
2POST text to /v2/check endpoint
3Download ngrams for better suggestions
4Browser extensions can use local server
5Supports 30+ languages
6Set as LibreOffice/Firefox extension backend

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  languagetool:
5    image: erikvl87/languagetool:latest
6    container_name: languagetool
7    restart: unless-stopped
8    ports:
9      - "${LT_PORT:-8010}:8010"
10    environment:
11      - langtool_languageModel=/ngrams
12      - Java_Xms=512m
13      - Java_Xmx=2g
14    volumes:
15      - ./ngrams:/ngrams
16EOF
17
18# 2. Create the .env file
19cat > .env << 'EOF'
20# LanguageTool Configuration
21LT_PORT=8010
22EOF
23
24# 3. Start the services
25docker compose up -d
26
27# 4. View logs
28docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/languagetool/run | bash

Troubleshooting

OutOfMemoryError during startup: Increase Java_Xmx value beyond 2g or reduce n-gram model size in ./ngrams volume
API returns empty suggestions: Verify n-gram models are properly mounted and downloaded to ./ngrams directory
High memory usage with multiple requests: Adjust Java_Xms and Java_Xmx values based on concurrent usage patterns
Language detection fails: Explicitly specify language parameter in API requests rather than relying on auto-detection
Slow response times on large texts: Break documents into smaller chunks or increase Java heap allocation
Container fails to start: Check that ./ngrams directory exists and has proper permissions for volume mounting

Community Notes

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